Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis: Prognostic and Therapeutic Implications.
Heart Fail Clin
; 17(3): 499-518, 2021 Jul.
Article
en En
| MEDLINE
| ID: mdl-34051979
ABSTRACT
Heart failure with preserved ejection fraction (HFpEF) is characterized by a high rate of hospitalization and mortality (up to 84% at 5 years), which are similar to those observed for heart failure with reduced ejection fraction (HFrEF). These epidemiologic data claim for the development of specific and innovative therapies to reduce the burden of morbidity and mortality associated with this disease. Compared with HFrEF, which is due to a primary myocardial damage (eg ischemia, cardiomyopathies, toxicity), a heterogeneous etiologic background characterizes HFpEF. The authors discuss these phenotypes and specificities for defining therapeutic strategies that could be proposed according to phenotypes.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Volumen Sistólico
/
Manejo de la Enfermedad
/
Aprendizaje Automático
/
Insuficiencia Cardíaca
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Heart Fail Clin
Año:
2021
Tipo del documento:
Article
País de afiliación:
Francia